Point-Based Planning for Multi-Objective POMDPs
Authors: Diederik Marijn Roijers, Shimon Whiteson, Frans A. Oliehoek
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show experimentally that OLSAR outperforms, both in terms of runtime and approximation quality, alternative methods and a variant of OLSAR that does not leverage reuse. |
| Researcher Affiliation | Academia | 1Informatics Institute, University of Amsterdam, The Netherlands 2Department of Computer Science, University of Liverpool, United Kingdom |
| Pseudocode | Yes | Algorithm 1: OLSAR(b0, η) and Algorithm 2: OCPerseus(A, B, w, η) |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper uses benchmark POMDP problems (Tiger, Maze20) and generates 'sampled beliefs' for its experiments. It does not provide concrete access information (link, DOI, citation) for a publicly available, pre-existing dataset that is 'trained' on in the traditional sense. |
| Dataset Splits | No | The paper mentions generating a 'reference set' for comparison, but it does not specify explicit training/validation/test dataset splits (e.g., percentages or counts of a fixed dataset) for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper describes the algorithms and their implementation details but does not provide specific version numbers for any software dependencies or libraries used. |
| Experiment Setup | Yes | We ran all algorithms with 100 belief points generated by random exploration, η = 1 × 10−6, and b0 set to a uniform distribution. |